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AI has shown to be a useful tool in my position at a company that specialises in company and market analysis for venture capital and corporate venture capital. I frequently filter through enormous volumes of data using AI-driven technologies to find possible startups to invest in. By evaluating market trends, analysing company profiles, and offering insights, these tools help me with market intelligence tasks. I can process this data efficiently and quickly by using GenAI, which makes it possible for me to make decisions more quickly and effectively. What once took days of manual research can now be achieved in hours.

Nevertheless, I think there’s room for improvement, especially in terms of these tools their ability to analyse websites. I regularly browse company websites in order to learn more about possible new ventures. Even though AI programs are fast at summarising simple text on a page, they frequently fail to capture the depth of these websites. For instance, a lot of websites have charts, tables, infographics, and videos that AI either ignores or interprets incorrectly. In addition, important information may be tucked away a few clicks from the main page, spread across multiple tabs, or incorporated into complicated site architecture, making it challenging for the current generation of AI tools to retrieve without human assistance.

It would be revolutionary if GenAI could process visual data more accurately, for example, by extracting important insights from charts, deciphering infographics that are embedded, or summarising video content. It would guarantee that no important data is missed during the analysis process and save time. In the same way, improving AI’s ability to navigate and extract content from deeper sections of websites would enable more detailed and automated data collection.

While Generative AI has significantly improved my efficiency, especially in handling text-based data, there’s room for improvement when it comes to multimodal analysis. Expanding the capabilities of these tools to handle videos, visual elements, and complex web structures would unlock even more potential and make them an even greater asset for market intelligence tasks.

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Ice cream sales are notoriously dependent on weather conditions. Six years ago De IJsMaker was established in Rotterdam to address the need for reasonably priced, superior ice cream. The brand is currently growing quickly and intends to go national. This success is primarily attributable to creative digital transformation and data-driven decision-making, using digital tools to streamline operations.

As it expanded, De IJsMaker ran into operational difficulties. In order to optimize staffing and inventory, the owner used the Cohelion Data Platform to analyze sales, weather forecasts, and local events. employing digital business models, where increasing customer satisfaction and efficiency is greatly aided by data analytics. To ensure they have the right inventory and personnel on hand, their Flavor Predictor tool, for example, uses weather and historical data to forecast which flavors will sell best on a particular day.

Even though De IJsMaker makes good use of data analytics, ice cream shops could benefit even more from the integration of AI and machine learning. To produce even more accurate forecasts, AI might examine more complex data patterns, such as social media trends and consumer behavior. This use of AI is related to the value that businesses can derive from information goods. AI might, for instance, improve dynamic pricing, provide promotions based on demand in real time, or make tailored flavor recommendations at shops to improve the user experience. Furthermore, research has demonstrated that conversational AI like chatbots for online ordering and reservations can greatly increase both operational effectiveness and customer satisfaction.

Expanding into AI does however come with challenges. It requires significant investment and technical expertise. However, as seen with their self-service kiosks, even incremental changes can greatly improve efficiency and sales. The potential for AI to further personalize and streamline operations is promising, but it needs careful implementation.

In my opinion the IJsMaker’s journey highlights the power of data in transforming a small business. The potential for AI in this context is exciting, offering a way to enhance customer experiences and operational efficiency. However, it raises questions about the balance between investment and return, especially for smaller businesses. Could the integration of AI in small businesses like De IJsMaker be a feasible and beneficial step? How might customers feel about their buying patterns being used to enhance their experience?

References: 

Conversational AI in Restaurants Case Study: How AI Is Transforming the Dining Experience. (2023, November 29). Pragmatic. https://www.pragmatic.digital/blog/conversational-ai-in-restaurants-case-study-how-ai-is-transforming-the-dining-experience

Gawkowski, E. (2024, July 8). AI in Restaurants: 10 Tips on How to Boost Your Restaurant with AI | UpMenu. UpMenu. https://www.upmenu.com/blog/ai-restaurant/

Suurenbroek, H. (2024, July 2). Read De IJsmaker case study and learn what they gained. Cohelion • Data Management and Integration Platform. https://cohelion.com/other-industries/de-ijsmaker/